lets_plot.geom_density2d

lets_plot.geom_density2d(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, kernel=None, adjust=None, bw=None, n=None, bins=None, binwidth=None, **other_args)

Display density function contour.

Parameters

  • mapping (FeatureSpec) – Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

  • data (dict or DataFrame) – The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

  • stat (str, default=’density2d’) – The statistical transformation to use on the data for this layer, as a string.

  • position (str or FeatureSpec) – Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

  • show_legend (bool, default=True) – False - do not show legend for this layer.

  • sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.

  • tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.

  • kernel (str, default=’gaussian’) – The kernel we use to calculate the density function. Choose among ‘gaussian’, ‘cosine’, ‘optcosine’, ‘rectangular’ (or ‘uniform’), ‘triangular’, ‘biweight’ (or ‘quartic’), ‘epanechikov’ (or ‘parabolic’).

  • bw (str or list of float) – The method (or exact value) of bandwidth. Either a string (choose among ‘nrd0’ and ‘nrd’), or a float array of length 2.

  • adjust (float) – Adjust the value of bandwidth my multiplying it. Changes how smooth the frequency curve is.

  • n (list of int) – The number of sampled points for plotting the function (on x and y direction correspondingly).

  • bins (int) – Number of levels.

  • binwidth (float) – Distance between levels.

  • other_args – Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns

Geom object specification.

Return type

LayerSpec

Note

geom_density2d() draws density function.

Computed variables:

  • ..group.. : number of density estimate contour line.

geom_density2d() understands the following aesthetics mappings:

  • x : x-axis coordinates.

  • y : y-axis coordinates.

  • alpha : transparency level of a layer. Understands numbers between 0 and 1.

  • color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.

  • size : lines width. Defines line width.

  • linetype : type of the line of border. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.

Examples

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import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
    geom_density2d()


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import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
    geom_density2d(aes(color='..group..'), size=1, show_legend=False) + \
    scale_color_brewer(type='seq', palette='GnBu', direction=-1)


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import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
p = ggplot({'x': x, 'y': y}, aes(x='x', y='y'))
bunch = GGBunch()
for i, bw in enumerate([.2, .4]):
    for j, n in enumerate([16, 256]):
        bunch.add_plot(p + geom_density2d(kernel='epanechikov', bw=bw, n=n) + \
                           ggtitle('bw={0}, n={1}'.format(bw, n)),
                       j * 400, i * 400, 400, 400)
bunch.show()


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import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
p = ggplot({'x': x, 'y': y}, aes(x='x', y='y'))
bunch = GGBunch()
for i, adjust in enumerate([1.5, 2.5]):
    for j, bins in enumerate([5, 15]):
        bunch.add_plot(p + geom_density2d(kernel='cosine', \
                                          adjust=adjust, bins=bins) + \
                           ggtitle('adjust={0}, bins={1}'.format(adjust, bins)),
                       j * 400, i * 400, 400, 400)
bunch.show()